How We Built an AI System That Automates Senior Solution Architect Workflows

30% of a senior architect's time goes into reading RFPs and writing proposals rather than designing systems or solving engineering problems.

A 50-page enterprise RFP lands in an inbox. Two hours later, the architect has concluded what they suspected on page three: React Native frontend, Node backend, PostgreSQL, and Stripe for payments. The decision was predictable, but the process was manual and time-consuming. 

This system assists with the presales cycle. You upload a client's documents—PDFs, Word docs, Excel sheets, or emails—and the system produces a draft technical proposal that an architect can review and refine in 15 minutes instead of writing from scratch for 3 hours.

The value extends beyond document generation to reasoning:

Agent 1: Requirement Interpreter

This agent parses raw, unstructured documents using format-specific tools (PyMuPDF, pandas, etc.). It extracts features, technical constraints, and non-functional requirements.

Agent 2: Solution Engine

This agent produces tech stack recommendations and effort estimates. It uses a hybrid approach: deterministic rules suggest the baseline stack (e.g., NestJS/PostgreSQL), while the LLM validates that choice against the specific client context. Effort estimation provides optimistic-to-pessimistic ranges (e.g., "120–210 hours") to reflect real-world uncertainty.

Agent 3: Self-Critique

Before a proposal is written, a critique agent reviews the solution for consistency. If an assumption contradicts a user input, the system sends a clarifying question back to the architect via WebSockets. Only when the critique approves does the pipeline proceed to generation.

Agent 4: Proposal Composer

Rule-based systems and LLMs complement each other: Conflict detection and effort mapping do not need an LLM—they need deterministic rules. LLMs handle ambiguity and prose generation.